Source code for thermosteam.properties.joback

# -*- coding: utf-8 -*-
'''
All data and functions for using the Joback group contribution method.
'''

from __future__ import division

__all__ = ['smarts_fragment', 'Joback', 'J_BIGGS_JOBACK_SMARTS', 
           'J_BIGGS_JOBACK_SMARTS_id_dict']

from collections import namedtuple, Counter
from math import exp
from pprint import pprint
from ..functional import horner_polynomial
from .utils import to_nums
try:
    from rdkit import Chem
    from rdkit.Chem import Descriptors
    from rdkit.Chem import AllChem
    from rdkit.Chem import rdMolDescriptors
    hasRDKit = True
except:
    # pragma: no cover
    hasRDKit = False

rdkit_missing = 'RDKit is not installed; it is required to use this functionality'

J_BIGGS_JOBACK_SMARTS = [["Methyl","-CH3", "[CX4H3]"],
["Secondary acyclic", "-CH2-", "[!R;CX4H2]"],
["Tertiary acyclic",">CH-", "[!R;CX4H]"],
["Quaternary acyclic", ">C<", "[!R;CX4H0]"],
["Primary alkene", "=CH2", "[CX3H2]"],
["Secondary alkene acyclic", "=CH-", "[!R;CX3H1;!$([CX3H1](=O))]"],
["Tertiary alkene acyclic", "=C<", "[$([!R;#6X3H0]);!$([!R;#6X3H0]=[#8])]"],
["Cumulative alkene", "=C=", "[$([CX2H0](=*)=*)]"],
["Terminal alkyne", u"≡CH","[$([CX2H1]#[!#7])]"],
["Internal alkyne",u"≡C-","[$([CX2H0]#[!#7])]"],
["Secondary cyclic", "-CH2- (ring)", "[R;CX4H2]"],
["Tertiary cyclic", ">CH- (ring)", "[R;CX4H]"],
["Quaternary cyclic", ">C< (ring)", "[R;CX4H0]"],
["Secondary alkene cyclic", "=CH- (ring)", "[R;CX3H1,cX3H1]"],
["Tertiary alkene cyclic", "=C< (ring)","[$([R;#6X3H0]);!$([R;#6X3H0]=[#8])]"],
["Fluoro", "-F", "[F]"],
["Chloro", "-Cl", "[Cl]"],
["Bromo", "-Br", "[Br]"],
["Iodo", "-I", "[I]"],
["Alcohol","-OH (alcohol)", "[OX2H;!$([OX2H]-[#6]=[O]);!$([OX2H]-a)]"],
["Phenol","-OH (phenol)", "[$([OX2H]-a)]"],
["Ether acyclic", "-O- (nonring)", "[OX2H0;!R;!$([OX2H0]-[#6]=[#8])]"],
["Ether cyclic", "-O- (ring)", "[#8X2H0;R;!$([#8X2H0]~[#6]=[#8])]"],
["Carbonyl acyclic", ">C=O (nonring)","[$([CX3H0](=[OX1]));!$([CX3](=[OX1])-[OX2]);!R]=O"],
["Carbonyl cyclic", ">C=O (ring)","[$([#6X3H0](=[OX1]));!$([#6X3](=[#8X1])~[#8X2]);R]=O"],
["Aldehyde","O=CH- (aldehyde)","[CX3H1](=O)"],
["Carboxylic acid", "-COOH (acid)", "[OX2H]-[C]=O"],
["Ester", "-COO- (ester)", "[#6X3H0;!$([#6X3H0](~O)(~O)(~O))](=[#8X1])[#8X2H0]"],
["Oxygen double bond other", "=O (other than above)","[OX1H0;!$([OX1H0]~[#6X3]);!$([OX1H0]~[#7X3]~[#8])]"],
["Primary amino","-NH2", "[NX3H2]"],
["Secondary amino acyclic",">NH (nonring)", "[NX3H1;!R]"],
["Secondary amino cyclic",">NH (ring)", "[#7X3H1;R]"],
["Tertiary amino", ">N- (nonring)","[#7X3H0;!$([#7](~O)~O)]"], 
["Imine acyclic","-N= (nonring)","[#7X2H0;!R]"],
["Imine cyclic","-N= (ring)","[#7X2H0;R]"],
["Aldimine", "=NH", "[#7X2H1]"],
["Cyano", "-CN","[#6X2]#[#7X1H0]"],
["Nitro", "-NO2", "[$([#7X3,#7X3+][!#8])](=[O])~[O-]"],
["Thiol", "-SH", "[SX2H]"],
["Thioether acyclic", "-S- (nonring)", "[#16X2H0;!R]"],
["Thioether cyclic", "-S- (ring)", "[#16X2H0;R]"]]

J_BIGGS_JOBACK_SMARTS_id_dict = {i+1: j[2] for i, j in enumerate(J_BIGGS_JOBACK_SMARTS)}
J_BIGGS_JOBACK_SMARTS_str_dict = {i[1]: i[2] for i in J_BIGGS_JOBACK_SMARTS}

# Shi Chenyang's JRGUI code indicates he left the following list of smarts in
# favor of those above by J Biggs
SHI_CHENYANG_JOBACK_SMARTS =  [
("-CH3", "[CH3;A;X4;!R]"),
("-CH2-", "[CH2;A;X4;!R]"),
(">CH-", "[CH1;A;X4;!R]"),
(">C<", "[CH0;A;X4;!R]"),
("=CH2", "[CH2;A;X3;!R]"),
("=CH-", "[CH1;A;X3;!R]"),
("=C<", "[CH0;A;X3;!R]"),
("=C=", "[$([CH0;A;X2;!R](=*)=*)]"),
("≡CH", "[$([CH1;A;X2;!R]#*)]"),
("≡C-", "[$([CH0;A;X2;!R]#*)]"),
("-CH2- (ring)", "[CH2;A;X4;R]"),
(">CH- (ring)", "[CH1;A;X4;R]"),
(">C< (ring)", "[CH0;A;X4;R]"),
("=CH- (ring)", "[CH1;X3;R]"),
("=C< (ring)", "[CH0;X3;R]"),
("-F", "[F]"),
("-Cl", "[Cl]"),
("-Br", "[Br]"),
("-I", "[I]"),
("-OH (alcohol)", "[O;H1;$(O-!@[C;!$(C=!@[O,N,S])])]"),
("-OH (phenol)", "[O;H1;$(O-!@c)]"),
("-O- (nonring)", "[OH0;X2;!R]"),
("-O- (ring)", "[OH0;X2;R]"),
(">C=O (nonring)", "[CH0;A;X3;!R]=O"),
(">C=O (ring)", "[CH0;A;X3;R]=O"),
("O=CH- (aldehyde)", "[CH;D2;$(C-!@C)](=O)"),
("-COOH (acid)", "[$(C-!@[A;!O])](=O)([O;H,-])"),
("-COO- (ester)", "C(=O)[OH0]"),
("=O (other than above)", "[OX1]"),
("-NH2", "[NH2;X3]"),
(">NH (nonring)", "[NH1;X3;!R]"),
(">NH (ring)", "[NH1;X3;R]"),
(">N- (nonring)", "[NH0;X3;!R]"),
("-N= (nonring)", "[NH0;X2;!R]"),
("-N= (ring)", "[NH0;X2;R]"),
("=NH", "[NH1;X2]"),
("-CN", "C#N"),
("-NO2", "N(=O)=O"),
("-SH", "[SH1]"),
("-S- (nonring)", "[SH0;!R]"),
("-S- (ring)", "[SH0;R]")]
SHI_CHENYANG_JOBACK_SMARTS_id_dict = {i+1: j[1] for i, j in enumerate(SHI_CHENYANG_JOBACK_SMARTS)}
SHI_CHENYANG_JOBACK_SMARTS_str_dict = {i[0]: i[1] for i in SHI_CHENYANG_JOBACK_SMARTS}

joback_data_txt = u'''-CH3 	0.0141 	-0.0012 	65 	23.58 	-5.10 	-76.45 	-43.96 	1.95E+1 	-8.08E-3 	1.53E-4 	-9.67E-8 	0.908 	2.373 	548.29 	-1.719
-CH2- 	0.0189 	0.0000 	56 	22.88 	11.27 	-20.64 	8.42 	-9.09E-1 	9.50E-2 	-5.44E-5 	1.19E-8 	2.590 	2.226 	94.16 	-0.199
>CH- 	0.0164 	0.0020 	41 	21.74 	12.64 	29.89 	58.36 	-2.30E+1 	2.04E-1 	-2.65E-4 	1.20E-7 	0.749 	1.691 	-322.15 	1.187
>C< 	0.0067 	0.0043 	27 	18.25 	46.43 	82.23 	116.02 	-6.62E+1 	4.27E-1 	-6.41E-4 	3.01E-7 	-1.460 	0.636 	-573.56 	2.307
=CH2 	0.0113 	-0.0028 	56 	18.18 	-4.32 	-9.630 	3.77 	2.36E+1 	-3.81E-2 	1.72E-4 	-1.03E-7 	-0.473 	1.724 	495.01 	-1.539
=CH- 	0.0129 	-0.0006 	46 	24.96 	8.73 	37.97 	48.53 	-8.00 	1.05E-1 	-9.63E-5 	3.56E-8 	2.691 	2.205 	82.28 	-0.242
=C< 	0.0117 	0.0011 	38 	24.14 	11.14 	83.99 	92.36 	-2.81E+1 	2.08E-1 	-3.06E-4 	1.46E-7 	3.063 	2.138 	n. a. 	n. a.
=C= 	0.0026 	0.0028 	36 	26.15 	17.78 	142.14 	136.70 	2.74E+1 	-5.57E-2 	1.01E-4 	-5.02E-8 	4.720 	2.661 	n. a. 	n. a.
≡CH 	0.0027 	-0.0008 	46 	9.20 	-11.18 	79.30 	77.71 	2.45E+1 	-2.71E-2 	1.11E-4 	-6.78E-8 	2.322 	1.155 	n. a. 	n. a.
≡C- 	0.0020 	0.0016 	37 	27.38 	64.32 	115.51 	109.82 	7.87 	2.01E-2 	-8.33E-6 	1.39E-9 	4.151 	3.302 	n. a. 	n. a.
-CH2- (ring)	0.0100 	0.0025 	48 	27.15 	7.75 	-26.80 	-3.68 	-6.03 	8.54E-2 	-8.00E-6 	-1.80E-8 	0.490 	2.398 	307.53 	-0.798
>CH- (ring)	0.0122 	0.0004 	38 	21.78 	19.88 	8.67 	40.99 	-2.05E+1 	1.62E-1 	-1.60E-4 	6.24E-8 	3.243 	1.942 	-394.29 	1.251
>C< (ring)	0.0042 	0.0061 	27 	21.32 	60.15 	79.72 	87.88 	-9.09E+1 	5.57E-1 	-9.00E-4 	4.69E-7 	-1.373 	0.644 	n. a. 	n. a.
=CH- (ring)	0.0082 	0.0011 	41 	26.73 	8.13 	2.09 	11.30 	-2.14 	5.74E-2 	-1.64E-6 	-1.59E-8 	1.101 	2.544 	259.65 	-0.702
=C< (ring)	0.0143 	0.0008 	32 	31.01 	37.02 	46.43 	54.05 	-8.25 	1.01E-1 	-1.42E-4 	6.78E-8 	2.394 	3.059 	-245.74 	0.912
-F 	0.0111 	-0.0057 	27 	-0.03 	-15.78 	-251.92 	-247.19 	2.65E+1 	-9.13E-2 	1.91E-4 	-1.03E-7 	1.398 	-0.670 	n. a. 	n. a.
-Cl 	0.0105 	-0.0049 	58 	38.13 	13.55 	-71.55 	-64.31 	3.33E+1 	-9.63E-2 	1.87E-4 	-9.96E-8 	2.515 	4.532 	625.45 	-1.814
-Br 	0.0133 	0.0057 	71 	66.86 	43.43 	-29.48 	-38.06 	2.86E+1 	-6.49E-2 	1.36E-4 	-7.45E-8 	3.603 	6.582 	738.91 	-2.038
-I 	0.0068 	-0.0034 	97 	93.84 	41.69 	21.06 	5.74 	3.21E+1 	-6.41E-2 	1.26E-4 	-6.87E-8 	2.724 	9.520 	809.55 	-2.224
-OH (alcohol) 	0.0741 	0.0112 	28 	92.88 	44.45 	-208.04 	-189.20 	2.57E+1 	-6.91E-2 	1.77E-4 	-9.88E-8 	2.406 	16.826 	2173.72 	-5.057
-OH (phenol) 	0.0240 	0.0184 	-25 	76.34 	82.83 	-221.65 	-197.37 	-2.81 	1.11E-1 	-1.16E-4 	4.94E-8 	4.490 	12.499 	3018.17 	-7.314
-O- (nonring) 	0.0168 	0.0015 	18 	22.42 	22.23 	-132.22 	-105.00 	2.55E+1 	-6.32E-2 	1.11E-4 	-5.48E-8 	1.188 	2.410 	122.09 	-0.386
-O- (ring) 	0.0098 	0.0048 	13 	31.22 	23.05 	-138.16 	-98.22 	1.22E+1 	-1.26E-2 	6.03E-5 	-3.86E-8 	5.879 	4.682 	440.24 	-0.953
>C=O (nonring) 	0.0380 	0.0031 	62 	76.75 	61.20 	-133.22 	-120.50 	6.45 	6.70E-2 	-3.57E-5 	2.86E-9 	4.189 	8.972 	340.35 	-0.350
>C=O (ring) 	0.0284 	0.0028 	55 	94.97 	75.97 	-164.50 	-126.27 	3.04E+1 	-8.29E-2 	2.36E-4 	-1.31E-7 	0. 	6.645 	n. a. 	n. a.
O=CH- (aldehyde) 	0.0379 	0.0030 	82 	72.24 	36.90 	-162.03 	-143.48 	3.09E+1 	-3.36E-2 	1.60E-4 	-9.88E-8 	3.197 	9.093 	740.92 	-1.713
-COOH (acid) 	0.0791 	0.0077 	89 	169.09 	155.50 	-426.72 	-387.87 	2.41E+1 	4.27E-2 	8.04E-5 	-6.87E-8 	11.051 	19.537 	1317.23 	-2.578
-COO- (ester) 	0.0481 	0.0005 	82 	81.10 	53.60 	-337.92 	-301.95 	2.45E+1 	4.02E-2 	4.02E-5 	-4.52E-8 	6.959 	9.633 	483.88 	-0.966
=O (other than above) 	0.0143 	0.0101 	36 	-10.50 	2.08 	-247.61 	-250.83 	6.82 	1.96E-2 	1.27E-5 	-1.78E-8 	3.624 	5.909 	675.24 	-1.340
-NH2 	0.0243 	0.0109 	38 	73.23 	66.89 	-22.02 	14.07 	2.69E+1 	-4.12E-2 	1.64E-4 	-9.76E-8 	3.515 	10.788 	n. a. 	n. a.
>NH (nonring) 	0.0295 	0.0077 	35 	50.17 	52.66 	53.47 	89.39 	-1.21 	7.62E-2 	-4.86E-5 	1.05E-8 	5.099 	6.436 	n. a. 	n. a.
>NH (ring) 	0.0130 	0.0114 	29 	52.82 	101.51 	31.65 	75.61 	1.18E+1 	-2.30E-2 	1.07E-4 	-6.28E-8 	7.490 	6.930 	n. a. 	n. a.
>N- (nonring) 	0.0169 	0.0074 	9 	11.74 	48.84 	123.34 	163.16 	-3.11E+1 	2.27E-1 	-3.20E-4 	1.46E-7 	4.703 	1.896 	n. a. 	n. a.
-N= (nonring) 	0.0255 	-0.0099 	n. a. 	74.60 	n. a. 	23.61 	n. a. 	n. a. 	n. a. 	n. a. 	n. a. 	n. a. 	3.335 	n. a. 	n. a.
-N= (ring) 	0.0085 	0.0076 	34 	57.55 	68.40 	55.52 	79.93 	8.83 	-3.84E-3 	4.35E-5 	-2.60E-8 	3.649 	6.528 	n. a. 	n. a.
=NH 	n. a. 	n. a. 	n. a. 	83.08 	68.91 	93.70 	119.66 	5.69 	-4.12E-3 	1.28E-4 	-8.88E-8 	n. a. 	12.169 	n. a. 	n. a.
-CN 	0.0496 	-0.0101 	91 	125.66 	59.89 	88.43 	89.22 	3.65E+1 	-7.33E-2 	1.84E-4 	-1.03E-7 	2.414 	12.851 	n. a. 	n. a.
-NO2 	0.0437 	0.0064 	91 	152.54 	127.24 	-66.57 	-16.83 	2.59E+1 	-3.74E-3 	1.29E-4 	-8.88E-8 	9.679 	16.738 	n. a. 	n. a.
-SH 	0.0031 	0.0084 	63 	63.56 	20.09 	-17.33 	-22.99 	3.53E+1 	-7.58E-2 	1.85E-4 	-1.03E-7 	2.360 	6.884 	n. a. 	n. a.
-S- (nonring) 	0.0119 	0.0049 	54 	68.78 	34.40 	41.87 	33.12 	1.96E+1 	-5.61E-3 	4.02E-5 	-2.76E-8 	4.130 	6.817 	n. a. 	n. a.
-S- (ring) 	0.0019 	0.0051 	38 	52.10 	79.93 	39.10 	27.76 	1.67E+1 	4.81E-3 	2.77E-5 	-2.11E-8 	1.557 	5.984 	n. a. 	n. a.'''

joback_groups_str_dict = {}
joback_groups_id_dict = {}
JOBACK = namedtuple('JOBACK', 'i, name, Tc, Pc, Vc, Tb, Tm, Hform, Gform, Cpa, Cpb, Cpc, Cpd, Hfus, Hvap, mua, mub')
for i, line in enumerate(joback_data_txt.split('\n')):
    parsed = to_nums(line.split('\t'))
    j = JOBACK(i+1, *parsed)
    joback_groups_str_dict[parsed[0]] = j
    joback_groups_id_dict[i+1] = j


[docs]def smarts_fragment(catalog, rdkitmol=None, smi=None): r'''Fragments a molecule into a set of unique groups and counts as specified by the `catalog`. The molecule can either be an rdkit molecule object, or a smiles string which will be parsed by rdkit. Returns a dictionary of groups and their counts according to the indexes of the catalog provided. Parameters ---------- catalog : dict Dictionary indexed by keys pointing to smarts strings, [-] rdkitmol : mol, optional Molecule as rdkit object, [-] smi : str, optional Smiles string representing a chemical, [-] Returns ------- counts : dict Dictionaty of integer counts of the found groups only, indexed by the same keys used by the catalog [-] success : bool Whether or not molecule was fully and uniquely fragmented, [-] status : str A string holding an explanation of why the molecule failed to be fragmented, if it fails; 'OK' if it suceeds. Notes ----- Raises an exception if rdkit is not installed, or `smi` or `rdkitmol` is not defined. Examples -------- Acetone: >>> smarts_fragment(catalog=J_BIGGS_JOBACK_SMARTS_id_dict, smi='CC(=O)C') ({24: 1, 1: 2}, True, 'OK') Sodium sulfate, (Na2O4S): >>> smarts_fragment(catalog=J_BIGGS_JOBACK_SMARTS_id_dict, smi='[O-]S(=O)(=O)[O-].[Na+].[Na+]') ({29: 4}, False, 'Did not match all atoms present') Propionic anhydride (C6H10O3): >>> smarts_fragment(catalog=J_BIGGS_JOBACK_SMARTS_id_dict, smi='CCC(=O)OC(=O)CC') ({1: 2, 2: 2, 28: 2}, False, 'Matched some atoms repeatedly: [4]') ''' if not hasRDKit: # pragma: no cover raise Exception(rdkit_missing) if rdkitmol is None and smi is None: raise Exception('Either an rdkit mol or a smiles string is required') if smi is not None: rdkitmol = Chem.MolFromSmiles(smi) if rdkitmol is None: status = 'Failed to construct mol' success = False return {}, success, status atom_count = len(rdkitmol.GetAtoms()) status = 'OK' success = True counts = {} all_matches = {} for key, smart in catalog.items(): patt = Chem.MolFromSmarts(smart) hits = rdkitmol.GetSubstructMatches(patt) if hits: all_matches[smart] = hits counts[key] = len(hits) matched_atoms = set() for i in all_matches.values(): for j in i: matched_atoms.update(j) if len(matched_atoms) != atom_count: status = 'Did not match all atoms present' success = False # Check the atom aount again, this time looking for duplicate matches (only if have yet to fail) if success: matched_atoms = [] for i in all_matches.values(): for j in i: matched_atoms.extend(j) if len(matched_atoms) < atom_count: status = 'Matched %d of %d atoms only' %(len(matched_atoms), atom_count) success = False elif len(matched_atoms) > atom_count: status = 'Matched some atoms repeatedly: %s' %( [i for i, c in Counter(matched_atoms).items() if c > 1]) success = False return counts, success, status
[docs]class Joback(object): r'''Class for performing chemical property estimations with the Joback group contribution method as described in [1]_ and [2]_. This is a very common method with low accuracy but wide applicability. This routine can be used with either its own automatic fragmentation routine, or user specified groups. It is applicable to organic compounds only, and has only 41 groups with no interactions between them. Each method's documentation describes its accuracy. The automatic fragmentation routine is possible only because of the development of SMARTS expressions to match the Joback groups by Dr. Jason Biggs. The list of SMARTS expressions was posted publically on the `RDKit mailing list <https://www.mail-archive.com/rdkit-discuss@lists.sourceforge.net/msg07446.html>`_. Parameters ---------- mol : rdkitmol or smiles str Input molecule for the analysis, [-] atom_count : int, optional The total number of atoms including hydrogen in the molecule; this will be counted by rdkit if it not provided, [-] MW : float, optional Molecular weight of the molecule; this will be calculated by rdkit if not provided, [g/mol] Tb : float, optional An experimentally known boiling temperature for the chemical; this increases the accuracy of the calculated critical point if provided. [K] Notes ----- Be sure to check the status of the automatic fragmentation; not all chemicals with the Joback method are applicable. Approximately 68% of chemcials in the thermo database seem to be able to be estimated with the Joback method. Examples -------- Analysis of Acetone: >>> J = Joback('CC(=O)C') >>> J.Hfus(J.counts) 5125.0 >>> J.Cpig(350) 84.69109750000001 >>> J.status 'OK' All properties can be obtained in one go with the `estimate` method: >>> pprint(J.estimate()) # doctest: +ELLIPSIS {'Cpig': <bound method Joback.Cpig of <thermo.joback.Joback object at 0x...>>, 'Cpig_coeffs': [7.520000000000003, 0.26084, -0.0001207, 1.545999999999998e-08], 'Gf': -154540.00000000003, 'Hf': -217829.99999999997, 'Hfus': 5125.0, 'Hvap': 29018.0, 'Pc': 4802499.604994407, 'Tb': 322.11, 'Tc': 500.5590049525365, 'Tm': 173.5, 'Vc': 0.0002095, 'mul': <bound method Joback.mul of <thermo.joback.Joback object at 0x...>>, 'mul_coeffs': [839.1099999999998, -14.99]} The results for propionic anhydride (if the status is not OK) should not be used. >>> J = Joback('CCC(=O)OC(=O)CC') >>> J.status 'Matched some atoms repeatedly: [4]' >>> J.Cpig(300) 175.85999999999999 None of the routines need to use the automatic routine; they can be used manually too: >>> Joback.Tb({1: 2, 24: 1}) 322.11 References ---------- .. [1] Joback, Kevin G. "A Unified Approach to Physical Property Estimation Using Multivariate Statistical Techniques." Thesis, Massachusetts Institute of Technology, 1984. .. [2] Joback, K.G., and R.C. Reid. "Estimation of Pure-Component Properties from Group-Contributions." Chemical Engineering Communications 57, no. 1-6 (July 1, 1987): 233-43. doi:10.1080/00986448708960487. ''' calculated_Cpig_coeffs = None calculated_mul_coeffs = None def __init__(self, mol, atom_count=None, MW=None, Tb=None): if type(mol) == Chem.rdchem.Mol: self.rdkitmol = mol else: self.rdkitmol = Chem.MolFromSmiles(mol) if atom_count is None: self.rdkitmol_Hs = Chem.AddHs(self.rdkitmol) self.atom_count = len(self.rdkitmol_Hs.GetAtoms()) else: self.atom_count = atom_count if MW is None: self.MW = rdMolDescriptors.CalcExactMolWt(self.rdkitmol_Hs) else: self.MW = MW self.counts, self.success, self.status = smarts_fragment(J_BIGGS_JOBACK_SMARTS_id_dict, rdkitmol=self.rdkitmol) if Tb is not None: self.Tb_estimated = self.Tb(self.counts) else: self.Tb_estimated = Tb
[docs] def estimate(self): '''Method to compute all available properties with the Joback method; returns their results as a dict. For the tempearture dependent values Cpig and mul, both the coefficients and objects to perform calculations are returned. ''' # Pre-generate the coefficients or they will not be returned self.mul(300) self.Cpig(300) estimates = {'Tb': self.Tb(self.counts), 'Tm': self.Tm(self.counts), 'Tc': self.Tc(self.counts, self.Tb_estimated), 'Pc': self.Pc(self.counts, self.atom_count), 'Vc': self.Vc(self.counts), 'Hf': self.Hf(self.counts), 'Gf': self.Gf(self.counts), 'Hfus': self.Hfus(self.counts), 'Hvap': self.Hvap(self.counts), 'mul': self.mul, 'mul_coeffs': self.calculated_mul_coeffs, 'Cpig': self.Cpig, 'Cpig_coeffs': self.calculated_Cpig_coeffs} return estimates
[docs] @staticmethod def Tb(counts): r'''Estimates the normal boiling temperature of an organic compound using the Joback method as a function of chemical structure only. .. math:: T_b = 198.2 + \sum_i {T_{b,i}} For 438 compounds tested by Joback, the absolute average error was 12.91 K and standard deviation was 17.85 K; the average relative error was 3.6%. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Returns ------- Tb : float Estimated normal boiling temperature, [K] Examples -------- >>> Joback.Tb({1: 2, 24: 1}) 322.11 ''' tot = 0.0 for group, count in counts.items(): tot += joback_groups_id_dict[group].Tb*count Tb = 198.2 + tot return Tb
[docs] @staticmethod def Tm(counts): r'''Estimates the melting temperature of an organic compound using the Joback method as a function of chemical structure only. .. math:: T_m = 122.5 + \sum_i {T_{m,i}} For 388 compounds tested by Joback, the absolute average error was 22.6 K and standard deviation was 24.68 K; the average relative error was 11.2%. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Returns ------- Tm : float Estimated melting temperature, [K] Examples -------- >>> Joback.Tm({1: 2, 24: 1}) 173.5 ''' tot = 0.0 for group, count in counts.items(): tot += joback_groups_id_dict[group].Tm*count Tm = 122.5 + tot return Tm
[docs] @staticmethod def Tc(counts, Tb=None): r'''Estimates the critcal temperature of an organic compound using the Joback method as a function of chemical structure only, or optionally improved by using an experimental boiling point. If the experimental boiling point is not provided it will be estimated with the Joback method as well. .. math:: T_c = T_b \left[0.584 + 0.965 \sum_i {T_{c,i}} - \left(\sum_i {T_{c,i}}\right)^2 \right]^{-1} For 409 compounds tested by Joback, the absolute average error was 4.76 K and standard deviation was 6.94 K; the average relative error was 0.81%. Appendix BI of Joback's work lists 409 estimated critical temperatures. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Tb : float, optional Experimental normal boiling temperature, [K] Returns ------- Tc : float Estimated critical temperature, [K] Examples -------- >>> Joback.Tc({1: 2, 24: 1}, Tb=322.11) 500.5590049525365 ''' if Tb is None: Tb = Joback.Tb(counts) tot = 0.0 for group, count in counts.items(): tot += joback_groups_id_dict[group].Tc*count Tc = Tb/(0.584 + 0.965*tot - tot*tot) return Tc
[docs] @staticmethod def Pc(counts, atom_count): r'''Estimates the critcal pressure of an organic compound using the Joback method as a function of chemical structure only. This correlation was developed using the actual number of atoms forming the molecule as well. .. math:: P_c = \left [0.113 + 0.0032N_A - \sum_i {P_{c,i}}\right ]^{-2} In the above equation, critical pressure is calculated in bar; it is converted to Pa here. 392 compounds were used by Joback in this determination, with an absolute average error of 2.06 bar, standard devaition 3.2 bar, and AARE of 5.2%. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] atom_count : int Total number of atoms (including hydrogens) in the molecule, [-] Returns ------- Pc : float Estimated critical pressure, [Pa] Examples -------- >>> Joback.Pc({1: 2, 24: 1}, 10) 4802499.604994407 ''' tot = 0.0 for group, count in counts.items(): tot += joback_groups_id_dict[group].Pc*count Pc = (0.113 + 0.0032*atom_count - tot)**-2 return Pc*1E5 # bar to Pa
[docs] @staticmethod def Vc(counts): r'''Estimates the critcal volume of an organic compound using the Joback method as a function of chemical structure only. .. math:: V_c = 17.5 + \sum_i {V_{c,i}} In the above equation, critical volume is calculated in cm^3/mol; it is converted to m^3/mol here. 310 compounds were used by Joback in this determination, with an absolute average error of 7.54 cm^3/mol, standard devaition 13.16 cm^3/mol, and AARE of 2.27%. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Returns ------- Vc : float Estimated critical volume, [m^3/mol] Examples -------- >>> Joback.Vc({1: 2, 24: 1}) 0.0002095 ''' tot = 0.0 for group, count in counts.items(): tot += joback_groups_id_dict[group].Vc*count Vc = 17.5 + tot return Vc*1E-6 # cm^3/mol to m^3/mol
[docs] @staticmethod def Hf(counts): r'''Estimates the ideal-gas enthalpy of formation at 298.15 K of an organic compound using the Joback method as a function of chemical structure only. .. math:: H_{formation} = 68.29 + \sum_i {H_{f,i}} In the above equation, enthalpy of formation is calculated in kJ/mol; it is converted to J/mol here. 370 compounds were used by Joback in this determination, with an absolute average error of 2.2 kcal/mol, standard devaition 2.0 kcal/mol, and AARE of 15.2%. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Returns ------- Hf : float Estimated ideal-gas enthalpy of formation at 298.15 K, [J/mol] Examples -------- >>> Joback.Hf({1: 2, 24: 1}) -217829.99999999997 ''' tot = 0.0 for group, count in counts.items(): tot += joback_groups_id_dict[group].Hform*count Hf = 68.29 + tot return Hf*1000 # kJ/mol to J/mol
[docs] @staticmethod def Gf(counts): r'''Estimates the ideal-gas Gibbs energy of formation at 298.15 K of an organic compound using the Joback method as a function of chemical structure only. .. math:: G_{formation} = 53.88 + \sum {G_{f,i}} In the above equation, Gibbs energy of formation is calculated in kJ/mol; it is converted to J/mol here. 328 compounds were used by Joback in this determination, with an absolute average error of 2.0 kcal/mol, standard devaition 4.37 kcal/mol, and AARE of 15.7%. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Returns ------- Gf : float Estimated ideal-gas Gibbs energy of formation at 298.15 K, [J/mol] Examples -------- >>> Joback.Gf({1: 2, 24: 1}) -154540.00000000003 ''' tot = 0.0 for group, count in counts.items(): tot += joback_groups_id_dict[group].Gform*count Gf = 53.88 + tot return Gf*1000 # kJ/mol to J/mol
[docs] @staticmethod def Hfus(counts): r'''Estimates the enthalpy of fusion of an organic compound at its melting point using the Joback method as a function of chemical structure only. .. math:: \Delta H_{fus} = -0.88 + \sum_i H_{fus,i} In the above equation, enthalpy of fusion is calculated in kJ/mol; it is converted to J/mol here. For 155 compounds tested by Joback, the absolute average error was 485.2 cal/mol and standard deviation was 661.4 cal/mol; the average relative error was 38.7%. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Returns ------- Hfus : float Estimated enthalpy of fusion of the compound at its melting point, [J/mol] Examples -------- >>> Joback.Hfus({1: 2, 24: 1}) 5125.0 ''' tot = 0.0 for group, count in counts.items(): tot += joback_groups_id_dict[group].Hfus*count Hfus = -0.88 + tot return Hfus*1000 # kJ/mol to J/mol
[docs] @staticmethod def Hvap(counts): r'''Estimates the enthalpy of vaporization of an organic compound at its normal boiling point using the Joback method as a function of chemical structure only. .. math:: \Delta H_{vap} = 15.30 + \sum_i H_{vap,i} In the above equation, enthalpy of fusion is calculated in kJ/mol; it is converted to J/mol here. For 368 compounds tested by Joback, the absolute average error was 303.5 cal/mol and standard deviation was 429 cal/mol; the average relative error was 3.88%. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Returns ------- Hvap : float Estimated enthalpy of vaporization of the compound at its normal boiling point, [J/mol] Examples -------- >>> Joback.Hvap({1: 2, 24: 1}) 29018.0 ''' tot = 0.0 for group, count in counts.items(): tot += joback_groups_id_dict[group].Hvap*count Hvap = 15.3 + tot return Hvap*1000 # kJ/mol to J/mol
[docs] @staticmethod def Cpig_coeffs(counts): r'''Computes the ideal-gas polynomial heat capacity coefficients of an organic compound using the Joback method as a function of chemical structure only. .. math:: C_p^{ig} = \sum_i a_i - 37.93 + \left[ \sum_i b_i + 0.210 \right] T + \left[ \sum_i c_i - 3.91 \cdot 10^{-4} \right] T^2 + \left[\sum_i d_i + 2.06 \cdot 10^{-7}\right] T^3 288 compounds were used by Joback in this determination. No overall error was reported. The ideal gas heat capacity values used in developing the heat capacity polynomials used 9 data points between 298 K and 1000 K. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Returns ------- coefficients : list[float] Coefficients which will result in a calculated heat capacity in in units of J/mol/K, [-] Examples -------- >>> c = Joback.Cpig_coeffs({1: 2, 24: 1}) >>> c [7.520000000000003, 0.26084, -0.0001207, 1.545999999999998e-08] >>> Cp = lambda T : c[0] + c[1]*T + c[2]*T**2 + c[3]*T**3 >>> Cp(300) 75.32642000000001 ''' a, b, c, d = 0.0, 0.0, 0.0, 0.0 for group, count in counts.items(): a += joback_groups_id_dict[group].Cpa*count b += joback_groups_id_dict[group].Cpb*count c += joback_groups_id_dict[group].Cpc*count d += joback_groups_id_dict[group].Cpd*count a -= 37.93 b += 0.210 c -= 3.91E-4 d += 2.06E-7 return [a, b, c, d]
[docs] @staticmethod def mul_coeffs(counts): r'''Computes the liquid phase viscosity Joback coefficients of an organic compound using the Joback method as a function of chemical structure only. .. math:: \mu_{liq} = \text{MW} \exp\left( \frac{ \sum_i \mu_a - 597.82}{T} + \sum_i \mu_b - 11.202 \right) 288 compounds were used by Joback in this determination. No overall error was reported. The liquid viscosity data used was specified to be at "several temperatures for each compound" only. A small and unspecified number of compounds were used in this estimation. Parameters ---------- counts : dict Dictionary of Joback groups present (numerically indexed) and their counts, [-] Returns ------- coefficients : list[float] Coefficients which will result in a liquid viscosity in in units of Pa*s, [-] Examples -------- >>> mu_ab = Joback.mul_coeffs({1: 2, 24: 1}) >>> mu_ab [839.1099999999998, -14.99] >>> MW = 58.041864812 >>> mul = lambda T : MW*exp(mu_ab[0]/T + mu_ab[1]) >>> mul(300) 0.0002940378347162687 ''' a, b = 0.0, 0.0 for group, count in counts.items(): a += joback_groups_id_dict[group].mua*count b += joback_groups_id_dict[group].mub*count a -= 597.82 b -= 11.202 return [a, b]
[docs] def Cpig(self, T): r'''Computes ideal-gas heat capacity at a specified temperature of an organic compound using the Joback method as a function of chemical structure only. .. math:: C_p^{ig} = \sum_i a_i - 37.93 + \left[ \sum_i b_i + 0.210 \right] T + \left[ \sum_i c_i - 3.91 \cdot 10^{-4} \right] T^2 + \left[\sum_i d_i + 2.06 \cdot 10^{-7}\right] T^3 Parameters ---------- T : float Temperature, [K] Returns ------- Cpig : float Ideal-gas heat capacity, [J/mol/K] Examples -------- >>> J = Joback('CC(=O)C') >>> J.Cpig(300) 75.32642000000001 ''' if self.calculated_Cpig_coeffs is None: self.calculated_Cpig_coeffs = Joback.Cpig_coeffs(self.counts) return horner_polynomial.function(reversed(self.calculated_Cpig_coeffs), T)
[docs] def mul(self, T): r'''Computes liquid viscosity at a specified temperature of an organic compound using the Joback method as a function of chemical structure only. .. math:: \mu_{liq} = \text{MW} \exp\left( \frac{ \sum_i \mu_a - 597.82}{T} + \sum_i \mu_b - 11.202 \right) Parameters ---------- T : float Temperature, [K] Returns ------- mul : float Liquid viscosity, [Pa*s] Examples -------- >>> J = Joback('CC(=O)C') >>> J.mul(300) 0.0002940378347162687 ''' if self.calculated_mul_coeffs is None: self.calculated_mul_coeffs = Joback.mul_coeffs(self.counts) a, b = self.calculated_mul_coeffs return self.MW*exp(a/T + b)